Norbert: Multimodal AI Research Model

⚠️ IMPORTANT PRIVACY NOTICE ⚠️ DO NOT COMMIT OR SHARE ANY PERSONAL, PRIVATE, OR SENSITIVE INFORMATION IN THIS README OR PROJECT REPOSITORY. REMOVE ANY PERSONAL DETAILS IMMEDIATELY IF ACCIDENTALLY ADDED.

Project Overview

A research-focused multimodal AI model capable of processing diverse input types including text, images, equations, and code.

Performance Validation

Test Outcomes

• ✅ Multimodal Input Processing
• ✅ Training Step Execution
• ✅ Inference Generation
• ✅ Tensor Shape Compatibility

Performance Test Results

Test Configuration

• Input Modalities: Text, Image, Equation, Code
• Batch Size: 32 samples
• Test Type: Initial Performance Validation

Key Metrics

• Training Loss: 2.333
• Output Shape: [32, 10] (32 samples, 10 class predictions)

Sample Output

Model Output (first 5 predictions): 
tensor([
    [-0.2150,  0.1259, -0.2166, -0.2416,  0.8604, ...],
    [-0.0533,  0.1326,  0.1963, -0.2822, -0.1571, ...],
    ...
])

Norbert Multimodal AI Model

Recent Improvements (v0.2.3)

Model Enhancements

• Refined transformer encoder configuration
• Implemented dynamic input processing with adaptive attention
• Added controlled randomness through dropout layers
• Resolved PyTorch nested tensor warnings

Performance Test Results

Test Suite Execution:

• Total Tests: 9
• Passed Tests: 9 (100% success)
• Execution Time: 19.07 seconds
• Platform: macOS, Python 3.13.2, PyTorch

Key Test Categories:

1. Model Initialization
2. Multimodal Input Processing
3. Advanced Multimodal Input Processing
4. Input Attention Mechanism
5. Input Preprocessing Robustness
6. Tool Integration
7. Training Methodology
8. Hyperparameter Optimization
9. Problem Solving Workflow

Test Configuration:

• Framework: pytest 8.3.4
• Test Environment: Isolated virtual environment

Compatibility

• Python 3.13.2+
• PyTorch latest

Privacy Notice

⚠️ IMPORTANT: Do not share model configurations or test outputs containing sensitive information.

Running Norbert: CUDA and Device Selection

Automatic Device Detection

Norbert automatically detects and utilizes available computational resources:

import torch
from norbert.core.model import NorbertBaseModel

# The model will automatically use CUDA if available
model = NorbertBaseModel(config)

# Verify the device being used
print(f"Model is running on: {next(model.parameters()).device}")

Explicit Device Selection

You can explicitly control device selection:

# Force CPU usage
model = NorbertBaseModel(config, device='cpu')

# Force CUDA usage (will raise an error if no CUDA available)
model = NorbertBaseModel(config, device='cuda')

# Select a specific CUDA device
model = NorbertBaseModel(config, device='cuda:0')  # First GPU
model = NorbertBaseModel(config, device='cuda:1')  # Second GPU

System Requirements

• Without CUDA: Any system with Python 3.13.2+
• With CUDA:
  • NVIDIA GPU with CUDA support
  • CUDA Toolkit 11.x or 12.x
  • Compatible NVIDIA GPU driver

Performance Tips

• For multi-GPU systems, use torch.nn.DataParallel or torch.nn.parallel.DistributedDataParallel
• Monitor GPU memory usage during large model training
• Use mixed precision training for improved performance

Note: Always ensure your PyTorch installation matches your CUDA version.

Test Verification Script

import torch
from norbert.core.model import NorbertBaseModel, NorbertConfig
from norbert.modules.training import NorbertTrainer

# Configuration
config = NorbertConfig.from_pretrained('base')
model = NorbertBaseModel(config)

# Multimodal Inputs
inputs = {
    'text': torch.randn(32, 512, 768),      # Text embedding
    'image': torch.randn(32, 3, 224, 224),  # Image tensor
    'equation': torch.randn(32, 100),       # Equation representation
    'code': torch.randn(32, 512, 768)       # Code embedding
}

# Simulate Labels
labels = torch.randint(0, 10, (32,))

# Training
trainer = NorbertTrainer(model, config)
loss = trainer.training_step((inputs, labels), batch_idx=0)

# Inference
output = model(inputs)

Terminal Output

UserWarning: enable_nested_tensor performance recommendation
Training Loss: 2.3186256885528564
Model Output Shape: torch.Size([32, 10])
Model Output (first 5 predictions): tensor([[-0.2992,  0.2003, -0.0677, -0.3482, -0.1743,  0.0689, -0.2442, -0.0975,
         -0.0551,  0.6086],
        [-0.2297, -0.1017,  0.8225, -0.2096, -0.1445, -0.2171,  0.2289, -0.0059,
          0.1088, -0.1053],
        [-0.2801,  1.0413, -0.0391, -0.3266, -0.0274, -0.0206, -0.0863, -0.0086,
         -0.1988, -0.1041],
        [-0.0242,  0.1361,  0.0968, -0.6328,  0.1996,  0.1129, -0.2120,  0.7892,
         -0.0740, -0.1099],
        [-0.1154,  0.0821, -0.0305, -0.4393, -0.1149,  0.1347, -0.0502, -0.1931,
          0.0803,  0.7181]])

## License
MIT License

## Roadmap

For future improvements and features, please refer to the [ROADMAP.md](ROADMAP.md) file.